Confidence intervals for predicted outcomes in regression models for categorical outcomes

We discuss methods for computing confidence intervals for predictions and discrete changes in predictions for regression models for categorical outcomes. The methods include endpoint transformation, the delta method, and bootstrapping. We also describe an update to prvalue and prgen from the SPost package, which adds the ability to compute confidence intervals. The article provides several examples that illustrate the application of these methods.


Issue Date:
2005
Publication Type:
Journal Article
DOI and Other Identifiers:
st0094 (Other)
PURL Identifier:
http://purl.umn.edu/117544
Published in:
Stata Journal, Volume 05, Number 4
Page range:
537-559
Total Pages:
23

Record appears in:



 Record created 2017-04-01, last modified 2017-04-27

Fulltext:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)